Publications

Abstract

Certain categories of language learners need feedback on the grammatical structure of sentences they wish to produce. In contrast with the usual NLP approach to this problem—parsing student-generated texts—we propose a generation-based approach aiming at preventing errors (“scaffolding”). In our ICALL system, students construct sentences by composing syntactic trees out of lexically anchored “treelets” via a graphical drag&drop user interface. A natural-language generator computes all possible grammatically well-formed sentences entailed by the student-composed tree, and intervenes immediately when the latter tree does not belong to the set of well-formed alternatives. Feedback is based on comparisons between the student-composed tree and the well-formed set. Frequently occurring errors are handled in terms of “malrules.” The system (implemented in JAVA and C++) currently focuses constituent order in German as L2.

Abstract

We present an overview of several corpus studies we carried out into the frequencies of argument NP orderings in the midfield of subordinate and main clauses of German. Comparing the corpus frequencies with grammaticality ratings published by Keller’s (2000), we observe a “grammaticality–frequency gap”: Quite a few argument orderings with zero corpus frequency are nevertheless assigned medium–range grammaticality ratings. We propose an explanation in terms of a two-factor theory. First, we hypothesize that the grammatical induction component needs a sufficient number of exposures to a syntactic pattern to incorporate it into its repertoire of more or less stable rules of grammar. Moderately to highly frequent argument NP orderings are likely have attained this status, but not their zero-frequency counterparts. This is why the latter argument sequences cannot be produced by the grammatical encoder and are absent from the corpora. Secondly, we assumed that an extraneous (nonlinguistic) judgment process biases the ratings of moderately grammatical linear order patterns: Confronted with such structures, the informants produce their own “ideal delivery” variant of the to-be-rated target sentence and evaluate the similarity between the two versions. A high similarity score yielded by this judgment then exerts a positive bias on the grammaticality rating—a score that should not be mistaken for an authentic grammaticality rating. We conclude that, at least in the linearization domain studied here, the goal of gaining a clear view of the internal grammar of language users is best served by a combined strategy in which grammar rules are founded on structures that elicit moderate to high grammaticality ratings and attain at least moderate usage frequencies.

Abstract

Experimental sentence comprehension studies have shown that superficially similar German clauses with verb-final word order elicit very different garden-path and ERP effects. We show that a computer implementation of the Unification Space parser (Vosse & Kempen, 2000) in the form of a localist-connectionist network can model the observed differences, at least qualitatively. The model embodies a parallel dynamic parser that, in contrast with existing models, does not distinguish between consecutive first-pass and reanalysis stages, and does not use semantic or thematic roles. It does use structural frequency data and animacy information.

Abstract

When you compare the behavior of two different age groups which are trying to master the same sensori-motor or cognitive skill, you are likely to discover varying learning routes: different stages, different intervals between stages, or even different orderings of stages. Such heterogeneous learning trajectories may be caused by at least six different types of factors: (1) Initial state: the kinds and levels of skills the learners have available at the onset of the learning episode. (2) Learning mechanisms: rule-based, inductive, connectionist, parameter setting, and so on. (3) Input and feedback characteristics: learning stimuli, information about success and failure. (4) Information processing mechanisms: capacity limitations, attentional biases, response preferences. (5) Energetic variables: motivation, emotional reactions. (6) Final state: the fine-structure of kinds and levels of subskills at the end of the learning episode. This applies to language acquisition as well. First and second language learners probably differ on all six factors. Nevertheless, the debate between advocates and opponents of the Fundamental Difference Hypothesis concerning L1 and L2 acquisition have looked almost exclusively at the first two factors. Those who believe that L1 learners have access to Universal Grammar whereas L2 learners rely on language processing strategies, postulate different learning mechanisms (UG parameter setting in L1, more general inductive strategies in L2 learning). Pienemann opposes this view and, based on his Processability Theory, argues that L1 and L2 learners start out from different initial states: they come to the grammar learning task with different structural hypotheses (SOV versus SVO as basic word order of German).